AI’s Next Data Center Challenge: Scaling Memory for the Inference Era

AI systems are shifting from training phase to inference phase, which requires different infrastructure priorities. Training focuses on compute speed and bandwidth to process model parameters quickly, while inference must efficiently serve millions of requests while managing large amounts of persistent memory like cached data and context. Data centers face a cost problem because memory is currently tightly coupled to compute, forcing operators to buy expensive processors just to gain additional memory capacity even when they don't need more computing power. A technology called Compute Express Link, or CXL, addresses this by decoupling memory from compute so that data centers can expand memory independently, enable memory pooling across systems, and use tiered memory architectures where different data types live in appropriately priced memory layers based on their access patterns.